// head_to_head
GPT-5.2 Chat vs GPT-6 Sol
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
GPT-6 Sol is the cheaper of the two; neither can be ranked on quality here.
Neither model has a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
openai
GPT-5.2 Chat
- Blended / 1M
- $4.81
- Context
- 128K
- Released
- Dec 10, 2025
- Overall score
- Not evaluated
openai
GPT-6 Sol
- Blended / 1M
- $4.00
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | GPT-5.2 Chat | GPT-6 Sol |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.81 | $4.00win |
| Input price / 1M | $1.75win | $2.00 |
| Output price / 1M | $14.00 | $10.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.175win | $0.200 |
| Context window | 128K | 1.1Mwin |
| Max output tokens | 32K | 128Kwin |
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | GPT-5.2 Chat | GPT-6 Sol |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,427/mo | $1,150/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,610/mo | $1,480/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,638/mo | $1,392/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,721/mo | $2,660/mo |
| Bulk classification 500 in / 20 out × 5M requests | $4,988/mo | $5,100/mo |
Which should you pick?
You need to fit large documents in one call
GPT-6 Sol
Wider context window — 1.1M against 128K.
GPT-5.2 Chat vs GPT-6 Sol FAQ
Which is better, GPT-5.2 Chat or GPT-6 Sol?
GPT-6 Sol is the cheaper of the two; neither can be ranked on quality here. Neither model has a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is GPT-5.2 Chat cheaper than GPT-6 Sol?
GPT-6 Sol is cheaper. On a 3:1 input:output blend, GPT-5.2 Chat lists at $4.81 per million tokens and GPT-6 Sol at $4.00 — GPT-6 Sol is 20% cheaper. Input and output are priced separately — GPT-5.2 Chat charges $1.75 in and $14.00 out, GPT-6 Sol charges $2.00 and $10.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does GPT-5.2 Chat or GPT-6 Sol have a bigger context window?
GPT-6 Sol has the larger context window: 128K for GPT-5.2 Chat against 1.1M for GPT-6 Sol. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do GPT-5.2 Chat and GPT-6 Sol support prompt caching?
Both publish a cached-input rate: $0.175 per million for GPT-5.2 Chat and $0.200 for GPT-6 Sol, against full input rates of $1.75 and $2.00. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.